Atypical antipsychotics, insulin resistance and weight; a meta-analysis of healthy volunteer studies

Burghardt, Kyle J.; Seyoum, Berhane; Mallisho, Abdullah; Burghardt, Paul; Kowluru, Renu A.; Yi, Zhengping

doi:10.1016/j.pnpbp.2018.01.004

Cited by 57 publications

(52 citation statements)

References 42 publications

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“…Insulin resistance seems to precede obesity in FEP ( 153 , 154 ), and antipsychotic-naïve FEP patients do not differ in BMI from controls ( 155 ). Antipsychotics have a more rapid effect on insulin sensitivity than on weight, which has also been shown in healthy volunteers exposed to antipsychotics ( 156 ). Furthermore, insulin resistance predicts more increase in weight in patients with FEP during the first year of antipsychotic treatment ( 147 ).…”

Section: Outcome Prediction: Current Evidencementioning

confidence: 82%

“…The prediction has been done based on the information from health records, either through an expert review ( 154 ) or using language analysis ( 155 ). Applications predicting the future in the predictive models could detect half of the suicide attempts and deaths during the next 60 days ( 156 ). These models were not developed specifically for FEP patients, however.…”

Section: Outcome Prediction: Current Evidencementioning

confidence: 99%

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Is It Possible to Predict the Future in First-Episode Psychosis?

et al. 2018

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The outcome of first-episode psychosis (FEP) is highly variable, ranging from early sustained recovery to antipsychotic treatment resistance from the onset of illness. For clinicians, a possibility to predict patient outcomes would be highly valuable for the selection of antipsychotic treatment and in tailoring psychosocial treatments and psychoeducation. This selective review summarizes current knowledge of prognostic markers in FEP. We sought potential outcome predictors from clinical and sociodemographic factors, cognition, brain imaging, genetics, and blood-based biomarkers, and we considered different outcomes, like remission, recovery, physical comorbidities, and suicide risk. Based on the review, it is currently possible to predict the future for FEP patients to some extent. Some clinical features—like the longer duration of untreated psychosis (DUP), poor premorbid adjustment, the insidious mode of onset, the greater severity of negative symptoms, comorbid substance use disorders (SUDs), a history of suicide attempts and suicidal ideation and having non-affective psychosis—are associated with a worse outcome. Of the social and demographic factors, male gender, social disadvantage, neighborhood deprivation, dysfunctional family environment, and ethnicity may be relevant. Treatment non-adherence is a substantial risk factor for relapse, but a small minority of patients with acute onset of FEP and early remission may benefit from antipsychotic discontinuation. Cognitive functioning is associated with functional outcomes. Brain imaging currently has limited utility as an outcome predictor, but this may change with methodological advancements. Polygenic risk scores (PRSs) might be useful as one component of a predictive tool, and pharmacogenetic testing is already available and valuable for patients who have problems in treatment response or with side effects. Most blood-based biomarkers need further validation. None of the currently available predictive markers has adequate sensitivity or specificity used alone. However, personalized treatment of FEP will need predictive tools. We discuss some methodologies, such as machine learning (ML), and tools that could lead to the improved prediction and clinical utility of different prognostic markers in FEP. Combination of different markers in ML models with a user friendly interface, or novel findings from e.g., molecular genetics or neuroimaging, may result in computer-assisted clinical applications in the near future.

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Section: Outcome Prediction: Current Evidencementioning

confidence: 82%

Section: Outcome Prediction: Current Evidencementioning

confidence: 99%

Is It Possible to Predict the Future in First-Episode Psychosis?

et al. 2018

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“…Antipsychotics, in particular second-generation or atypical antipsychotics (AAPs), increase the risk of metabolic syndrome 2-3-fold in patients with schizophrenia due to their effects on weight and insulin resistance [ 1 – 4 ]. The metabolic syndrome consists of a cluster of metabolic disorders that include obesity, dyslipidemia, hypertension, and insulin resistance [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

The Influence of Metabolic Syndrome and Sex on the DNA Methylome in Schizophrenia

Burghardt

Goodrich

Lines

et al. 2018

International Journal of Genomics

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Introduction The mechanism by which metabolic syndrome occurs in schizophrenia is not completely known; however, previous work suggests that changes in DNA methylation may be involved which is further influenced by sex. Within this study, the DNA methylome was profiled to identify altered methylation associated with metabolic syndrome in a schizophrenia population on atypical antipsychotics. Methods Peripheral blood from schizophrenia subjects was utilized for DNA methylation analyses. Discovery analyses (n = 96) were performed using an epigenome-wide analysis on the Illumina HumanMethylation450K BeadChip based on metabolic syndrome diagnosis. A secondary discovery analysis was conducted based on sex. The top hits from the discovery analyses were assessed in an additional validation set (n = 166) using site-specific methylation pyrosequencing. Results A significant increase in CDH22 gene methylation in subjects with metabolic syndrome was identified in the overall sample. Additionally, differential methylation was found within the MAP3K13 gene in females and the CCDC8 gene within males. Significant differences in methylation were again observed for the CDH22 and MAP3K13 genes, but not CCDC8, in the validation sample set. Conclusions This study provides preliminary evidence that DNA methylation may be associated with metabolic syndrome and sex in schizophrenia.

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“…Considerable evidence reveals that the presence of SMI may be an independent risk factor for the development of insulin resistance and metabolic disease ( 5 , 6 ). When examined in healthy controls in laboratory settings, antipsychotic drugs have direct effects on glucose metabolism in Burghardt et al ( 7 ). Even drugs considered “metabolically neutral” like aripiprazole (ARPZ) may promote glucose intolerance ( 8 ).…”

Section: Introductionmentioning

confidence: 99%

Does the Time of Drug Administration Alter the Metabolic Risk of Aripiprazole?

et al. 2018

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Antipsychotic drugs cause metabolic abnormalities through a mechanism that involves antagonism of D2 dopamine receptors (D2R). Under healthy conditions, insulin release follows a circadian rhythm and is low at night, and in pancreatic beta-cells, D2Rs negatively regulate insulin release. Since they are sedating, many antipsychotics are dosed at night. However, the resulting reduction in overnight D2R activity may disrupt 24 h rhythms in insulin release, potentially exacerbating metabolic dysfunction. We examined retrospective clinical data from patients treated over approximately 1 year with the antipsychotic drug aripiprazole (ARPZ), a D2R partial agonist. To identify effects of timing on metabolic risk, we found cases treated with ARPZ either in the morning (n = 90) or at bedtime (n = 53), and compared hemoglobin A1c, and six secondary metabolic parameters across the two groups. After controlling for demographic and clinical factors, patients treated with ARPZ at night had a significant decrease in HDL cholesterol, while in patients who took ARPZ in the morning had no change. There was a non-significant trend toward higher serum triglycerides in the patients treated with ARPZ at night vs. morning. There were no group differences in hemoglobin A1c, BMI, total cholesterol, LDL cholesterol, or blood pressure. Patients taking APPZ at night developed a worse lipid profile, with lower HDL cholesterol and a trend toward higher triglycerides. These changes may pose additional metabolic risk factors compared to those who take ARPZ in the morning. Interventions based on drug timing may reduce some of the adverse metabolic consequences of antipsychotic drugs.

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Atypical antipsychotics, insulin resistance and weight; a meta-analysis of healthy volunteer studies

Cited by 57 publications

References 42 publications

Is It Possible to Predict the Future in First-Episode Psychosis?

Is It Possible to Predict the Future in First-Episode Psychosis?

The Influence of Metabolic Syndrome and Sex on the DNA Methylome in Schizophrenia

Does the Time of Drug Administration Alter the Metabolic Risk of Aripiprazole?

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